code: def test_integration_binary_and_mulitlabel_inputs(
      n_times, test_data_integration_binary_and_multilabel
  ):
      (
          y_pred,
          y,
          batch_size,
      ) = test_data_integration_binary_and_multilabel
  
      def update_fn(engine, batch):
          idx = (engine.state.iteration - 1) * batch_size
          y_true_batch = np_y[idx : idx + batch_size]
          y_pred_batch = np_y_pred[idx : idx + batch_size]
          return torch.from_numpy(y_pred_batch), torch.from_numpy(
              y_true_batch
          )
  
      engine = Engine(update_fn)
  
      ap_metric = AveragePrecision()
      ap_metric.attach(engine, "ap")
  
      np_y = y.numpy()
      np_y_pred = y_pred.numpy()
  
      np_ap = average_precision_score(np_y, np_y_pred)
  
      data = list(range(y_pred.shape[0] // batch_size))
      ap = engine.run(data, max_epochs=1).metrics["ap"]
  
      assert isinstance(ap, float)
      assert np_ap == pytest.approx(ap)
  
imports: import os
  from unittest.mock import patch
  import pytest
  import sklearn
  import torch
  from sklearn.metrics import average_precision_score
  import ignite.distributed
  from ignite.contrib.metrics import AveragePrecision
  from ignite.engine import Engine
  from ignite.exceptions import NotComputableError
label_desc: Use image node_id 9 for calling a global function with example usage: test_integration_binary_and_mulitlabel_inputs(n_times, test_data_integration_binary_and_multilabel) and returns: torch,  torch